The quantification of lexical semantic relatedness has many applications in NLP, and many different measures have been proposed. We evaluate five of these measures, all of which use WordNet as their central resource, by comparing their performance in detecting and correcting real-word spelling errors. An information-content--based measure proposed by Jiang and Conrath is found superior to those proposed by Hirst and St-Onge, Leacock and Chodorow, Lin, and Resnik. In addition, we explain why distributional similarity is not an adequate proxy for lexical semantic relatedness.
Download:
PDF file (606 Kb).
Request paper copy: Send request with
postal address to gh@cs.txrxntx.edu (replace the Xs with Os).
[an error occurred while processing this directive]
[an error occurred while processing this directive]